An Exploration of the Fuzzy Inference System for the Daily Trading Decision and Its Performance Analysis Based on Fuzzy MCDM Methods

In the field of stock trading, forecasting the future stock price movement is an essential yet challenging area. Precise prediction of the stock market movement is warranted to have a deterministic return. In this regard, the current study attempts to develop a new indicator based on the Fuzzy Inference System (FIS). From the historical stock price, the technical indicators such as Moving Average Convergence and Divergence, Relative Strength Index, Stochastic Oscillator, and On-Balance-Volume values are calculated and fuzzified. FIS framework is developed through fuzzy rules that are based on the expert’s opinion on the fuzzified technical indicators. The FIS recommends daily trading decisions such as buy, hold, and sell signals. To validate the proposed FIS framework, the daily stock price of the top 25 companies listed in the NASDAQ for the period from 2015 to 2019 have been used. Using statistical methods and Fuzzy Multi-criteria Decision Making (FMCDM) methods, the performance of the proposed FIS model has been compared with the existing technical indicators as well as Buy and Hold strategy. Finally, the correlation of the FMCDM approaches is evaluated through Spearman’s and Kendall’s rank correlation.